锂电池实时遗忘因子在线参数辨识与状态估计  

Online parameter identification and state estimation of lithium batteries based on real-time forgetting factor

作　　者：阚英哲 杨敏 孙华泽 谢云飞 KAN Yingzhe;YANG Min;SUN Huaze;XIE Yunfei(School of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学机械工程学院,重庆400054

出　　处：《重庆理工大学学报（自然科学）》2024年第11期18-26,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(52205052);重庆市教委科学技术研究项目(KJQN202201125);重庆理工大学科研启动基金项目(2021ZDZ015)。

摘　　要：SOC(state-of-charge)作为电动汽车能量管理、续驶里程估算、动力系统控制等功能的重要依据,其准确性至关重要,而模型参数的精确程度则是准确判定动力电池SOC的核心基础。传统的离线参数辨识使用固定的模型参数描述电池的性能及其响应,然而,在不同的放电倍率以及不同倍率持续时间等因素的影响下,电池内部的某些参数也会相应地发生变化,如果继续采用固定的模型参数,对电池状态的预测和估计就会出现较大的偏差。提出了一种自调节遗忘因子递推最小二乘法,在线辨识出电池模型的各项参数,将得到的模型参数导入扩展卡尔曼滤波算法中,用于实时估计电池的SOC。通过对比分析和验证,该方法在不同工况下SOC估计误差都能收敛在1%以内,证明具有良好的模型参数辨识精度和鲁棒性,可以显著提高SOC的估算精度。The accuracy of State of Charge(SOC)is an important basis for energy management,range estimation,power system control,and other functions of electric vehicles.The accurate model parameters are the very foundations for correctly determining the SOC of power batteries.Traditional offline parameter identification uses fixed model parameters to describe the performance and response of batteries.However,under the influence of different discharge rates and durations,some internal parameters of the batteries experience changes accordingly.If fixed model parameters are employed,significant deviations may occur in predicting and estimating the battery state.We propose a self-adjusting forgetting factor recursive least squares method to identify the various parameters of the battery model online,and the obtained model parameters are imported into the extended Kalman filtering algorithm for real-time estimation of the battery’s SOC.Through comparative analysis and verification,our method converges to within 1%of SOC estimation error under different operating conditions,demonstrating fairly good model parameter identification accuracy and robustness,and significantly improving SOC estimation accuracy.

关 键 词：SOC估计 最小二乘法 扩展卡尔曼滤波 参数辨识 

分 类 号：U46[机械工程—车辆工程]